Ari E. Kahn

Meta-learning is expressed through altered prefrontal cortical dynamics

This study shows that meta-learning in rats involves changes in prefrontal cortical dynamics that enable rule-based reward learning.

Time-resolved functional connectivity during visuomotor graph learning

The paper investigates how neural responses differ during learning of modular and non-modular graph structures using fMRI data, finding distinct connectivity patterns associated with performance on different graph types.

Trial-by-trial learning of successor representations in human behavior

The paper investigates how humans learn successor representations using trial-by-trial reaction times in graph sequence learning tasks, finding that eligibility trace mechanisms play a predominant role.

Network structure influences the strength of learned neural representations

The paper shows that the structure of a temporal sequence affects how well stimuli are represented in the brain, with modular graphs leading to better prediction and higher dimensionality in visual areas.

Humans rationally balance detailed and temporally abstract world models

The paper investigates how humans balance detailed and temporally abstract world models during learning, finding individual variation in strategy use and dynamic adaptation based on environmental predictability.

Hippocampal representations of alternative possibilities are flexibly generated to meet cognitive demands

The paper shows that rat hippocampus generates flexible representations of alternative spatial locations during navigation, modulated by changing cognitive demands.

Measuring Neuronal Avalanches to inform Brain-Computer Interfaces

This study uses neuronal avalanches to improve brain-computer interfaces by showing their task-specific differences can be decoded better than current methods.

Dual credit assignment processes underlie dopamine signals in a complex spatial environment

The paper shows dopamine signals in rats update value through two learning processes during foraging in a complex environment.

Alprazolam modulates persistence energy during emotion processing in first-degree relatives of individuals with schizophrenia: a network control study

This study uses network control theory to examine how alprazolam affects persistence energy during emotion processing in schizophrenia relatives, revealing compensatory mechanisms and neuromodulatory relationships.

Neurophysiological evidence for cognitive map formation during sequence learning

This study links cognitive map formation during sequence learning to neural activity in the temporal lobe using behavioral modeling and iEEG recordings.

Predictive control of electrophysiological network architecture using direct, single-node neurostimulation in humans

This study uses network control theory to predictably reconfigure brain network interactions through targeted neurostimulation in humans.

White Matter Network Architecture Guides Direct Electrical Stimulation Through Optimal State Transitions

This paper uses network control theory to model how white matter tracts guide electrical stimulation in the brain, showing that real white matter structures better predict state transitions than artificial models.

Process reveals structure: How a network is traversed mediates expectations about its architecture

The paper shows that temporal structure in network traversal affects how well learners infer network architecture.